MVMS: RNN based Pro-Active Resource Scaling in Cloud Environment

Abstract

Cloud computing offers various services to its users, ranging from infrastructure, and system development environment, to software as a service over the internet. Having such promising services available over the internet consistently, it has become an ever-demanding facility. As a reliable services provider, a cloud service provider (CSP) needs to deliver its services seamlessly to users and is also required to optimally utilize the resources. Optimal resource utilization eliminates over and under-provisioning and improves the availability of cloud services. Therefore, it is a great need to have a model allowing CSP to systematize its resources to cater to customers' demands. Such a model should be computationally light and quick enough to produce effective results. In this work, a simple yet effective neural network-based resource prediction model named MVMS is proposed, which enables a CSP to predict the customer's resource demand in advance. The results show that compared to GRU, the proposed Multi-Variate Multi-Step (MVMS) model predicts the resources accurately. Thus, CSP can schedule the resources precisely and process real-time requests of users. Experiments on the bitbrains dataset indicate that the proposed MVMS resource prediction model is quick and accurate, with lower RMSE and MAE values.